Crop yield prediction using aggregated rainfall-based modular artificial neural networks and support vector regression

Ekaansh Khosla (), Ramesh Dharavath () and Rashmi Priya ()
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Ekaansh Khosla: Indian Institute of Technology (ISM)
Ramesh Dharavath: Indian Institute of Technology (ISM)
Rashmi Priya: Indian Institute of Technology (ISM)

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2020, vol. 22, issue 6, No 36, 5687-5708

Abstract: Abstract At the present time, one of the most important sources of survival as well as the most crucial factor in the growth of Indian economy is agriculture. More than 70% of the Indian population is involved in agricultural activities. The crop yield prediction is one of the most desirable yet challenging tasks for every nation. Nowadays, due to the unpredictable climatic changes, farmers are struggling to obtain a good amount of yield from the crops. To feed the increasing population of India, there is a need to incorporate the latest technology and tools in the agricultural sector. This study focuses on the prediction of major kharif crops in Andhra Pradesh’s one of the largest costal districts: Visakhapatnam. As rainfall is the main factor in determining amount of kharif crop production, in this study, first we predict the amount of monsoon rainfall by using modular artificial neural networks (MANNs), and then, we predict the amount of major kharif crops that can be yielded by using the rainfall data and area given to that particular crop by using support vector regression (SVR). By using the methodology of MANNs-SVR, proper agricultural strategies can be made in order to increase the yield of the crops. Comparison with other machine learning algorithms has been done which shows that the proposed methodology outperforms in predicting the instances for kharif crop production.

Keywords: Agriculture; Crop modeling; Machine learning; Support vector regression; Yield prediction (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10668-019-00445-x

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